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Comprehensive exploration of graphically defined reaction spaces
Existing reaction transition state (TS) databases are comparatively small and lack chemical diversity. Here, this data gap has been addressed using the concept of a graphically-defined model reaction to comprehensively characterize a reaction space associated with C, H, O, and N containing molecules...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10025260/ https://www.ncbi.nlm.nih.gov/pubmed/36935430 http://dx.doi.org/10.1038/s41597-023-02043-z |
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author | Zhao, Qiyuan Vaddadi, Sai Mahit Woulfe, Michael Ogunfowora, Lawal A. Garimella, Sanjay S. Isayev, Olexandr Savoie, Brett M. |
author_facet | Zhao, Qiyuan Vaddadi, Sai Mahit Woulfe, Michael Ogunfowora, Lawal A. Garimella, Sanjay S. Isayev, Olexandr Savoie, Brett M. |
author_sort | Zhao, Qiyuan |
collection | PubMed |
description | Existing reaction transition state (TS) databases are comparatively small and lack chemical diversity. Here, this data gap has been addressed using the concept of a graphically-defined model reaction to comprehensively characterize a reaction space associated with C, H, O, and N containing molecules with up to 10 heavy (non-hydrogen) atoms. The resulting dataset is composed of 176,992 organic reactions possessing at least one validated TS, activation energy, heat of reaction, reactant and product geometries, frequencies, and atom-mapping. For 33,032 reactions, more than one TS was discovered by conformational sampling, allowing conformational errors in TS prediction to be assessed. Data is supplied at the GFN2-xTB and B3LYP-D3/TZVP levels of theory. A subset of reactions were recalculated at the CCSD(T)-F12/cc-pVDZ-F12 and ωB97X-D2/def2-TZVP levels to establish relative errors. The resulting collection of reactions and properties are called the Reaction Graph Depth 1 (RGD1) dataset. RGD1 represents the largest and most chemically diverse TS dataset published to date and should find immediate use in developing novel machine learning models for predicting reaction properties. |
format | Online Article Text |
id | pubmed-10025260 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100252602023-03-21 Comprehensive exploration of graphically defined reaction spaces Zhao, Qiyuan Vaddadi, Sai Mahit Woulfe, Michael Ogunfowora, Lawal A. Garimella, Sanjay S. Isayev, Olexandr Savoie, Brett M. Sci Data Data Descriptor Existing reaction transition state (TS) databases are comparatively small and lack chemical diversity. Here, this data gap has been addressed using the concept of a graphically-defined model reaction to comprehensively characterize a reaction space associated with C, H, O, and N containing molecules with up to 10 heavy (non-hydrogen) atoms. The resulting dataset is composed of 176,992 organic reactions possessing at least one validated TS, activation energy, heat of reaction, reactant and product geometries, frequencies, and atom-mapping. For 33,032 reactions, more than one TS was discovered by conformational sampling, allowing conformational errors in TS prediction to be assessed. Data is supplied at the GFN2-xTB and B3LYP-D3/TZVP levels of theory. A subset of reactions were recalculated at the CCSD(T)-F12/cc-pVDZ-F12 and ωB97X-D2/def2-TZVP levels to establish relative errors. The resulting collection of reactions and properties are called the Reaction Graph Depth 1 (RGD1) dataset. RGD1 represents the largest and most chemically diverse TS dataset published to date and should find immediate use in developing novel machine learning models for predicting reaction properties. Nature Publishing Group UK 2023-03-20 /pmc/articles/PMC10025260/ /pubmed/36935430 http://dx.doi.org/10.1038/s41597-023-02043-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Zhao, Qiyuan Vaddadi, Sai Mahit Woulfe, Michael Ogunfowora, Lawal A. Garimella, Sanjay S. Isayev, Olexandr Savoie, Brett M. Comprehensive exploration of graphically defined reaction spaces |
title | Comprehensive exploration of graphically defined reaction spaces |
title_full | Comprehensive exploration of graphically defined reaction spaces |
title_fullStr | Comprehensive exploration of graphically defined reaction spaces |
title_full_unstemmed | Comprehensive exploration of graphically defined reaction spaces |
title_short | Comprehensive exploration of graphically defined reaction spaces |
title_sort | comprehensive exploration of graphically defined reaction spaces |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10025260/ https://www.ncbi.nlm.nih.gov/pubmed/36935430 http://dx.doi.org/10.1038/s41597-023-02043-z |
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